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CIG: The Collaborative Information Gathering Project (HCI)

FM: micro-Chart Tools

Handheld Devices/Laid-Back Computing (HCI)

LIDS: The Application of Large Interactive Display Surfaces (HCI)

NZDL: The New Zealand Digital Library Project

The B/Z Testing Tools Project

The University of Waikato Usability Laboratory (HCI)

WAND: The Waikato Applied Network Dynamics Group

WEKA: The Waikato Environment for Knowledge Analysis Project

Projects
WEKA: The Waikato Environment for Knowledge Analysis Project

Geoff Holmes, John Cleary, Sally Jo Cunningham, Eibe Frank, Mark Hall, Lyn Hunt (Statistics), Margaret Jefferies, Bob McQueen, Bernhard Pfahringer, Tony Smith, Ian Witten

An exciting and potentially far-reaching development in contemporary computer science is the invention and application of methods of machine learning. These enable a computer program to automatically analyse a large body of data and decide what information is most relevant. This crystallised information can then be used to help people make decisions faster and more accurately.

One of the central problems of the information age is dealing with the enormous explosion in the amount of raw information that is available. Machine learning (ML) has the potential to sift through this mass of information and convert it into knowledge that people can use. So far, however, it has been used mainly on small problems under well controlled conditions. Our aim is to bring the technology out of the laboratory and provide solutions that can make a difference to people.

The overall goal of our research in machine learning is to build a state-of-the-art facility for developing and comparing techniques of ML, and to investigate their application to key areas of the New Zealand economy.

Our team has incorporated several standard ML techniques into an experimental software "workbench" called WEKA, for Waikato Environment for Knowledge Analysis. With it, a specialist in a particular field will be able to use ML to derive useful knowledge from databases that are far too large to be analysed by hand. At present, WEKA's users are ML researchers, but ultimately they will be industrial scientists. It has already been used to help determine what information dairy farmers use in deciding which cows to keep in their herds. Other problems to be tackled include analysing milk production and identifying the factors that govern the cyclical patterns of opossum population. The workbench has already attracted international interest; when it is mature, it will be distributed widely.

Further information about this project can be found at: http://www.cs.waikato.ac.nz/ml/.

Student Project Members
Aaron Bluett, Thomas Johnson, Richard Kirkby, Lance Paine, Gabi Schmidberger, Malcome Were, Xin Xu

  2007 FCMS. The University of Waikato - Te Whare Wananga o Waikato